code: def test_skim_separator_forward_backward_real(
      input_dim,
      layer,
      causal,
      unit,
      dropout,
      num_spk,
      nonlinear,
      mem_type,
      segment_size,
      seg_overlap,
  ):
      model = SkiMSeparator(
          input_dim=input_dim,
          causal=causal,
          num_spk=num_spk,
          nonlinear=nonlinear,
          layer=layer,
          unit=unit,
          segment_size=segment_size,
          dropout=dropout,
          mem_type=mem_type,
          seg_overlap=seg_overlap,
      )
      model.train()
  
      x = torch.rand(2, 10, input_dim)
      x_lens = torch.tensor([10, 8], dtype=torch.long)
  
      masked, flens, others = model(x, ilens=x_lens)
  
      assert isinstance(masked[0], Tensor)
      assert len(masked) == num_spk
  
      masked[0].abs().mean().backward()
  
file_path: espnet/test/espnet2/enh/separator/test_skim_separator.py
imports: import pytest
  import torch
  from torch import Tensor
  from torch_complex import ComplexTensor
  from espnet2.enh.separator.skim_separator import SkiMSeparator
label_desc: Use image node_id 2 for calling a global function with example usage: test_skim_separator_forward_backward_real(input_dim, layer, causal, unit, dropout, num_spk, nonlinear, mem_type, segment_size, seg_overlap) without return types